scholarly journals Multistage graph problems on a global budget

2021 ◽  
Vol 868 ◽  
pp. 46-64
Author(s):  
Klaus Heeger ◽  
Anne-Sophie Himmel ◽  
Frank Kammer ◽  
Rolf Niedermeier ◽  
Malte Renken ◽  
...  
Keyword(s):  
Global Budget ◽  
Graph Problems

Dual parameterization and parameterized approximability of subset graph problems

2017 ◽  
Vol 51 (1) ◽  
pp. 261-266 ◽  
Author(s):  
Édouard Bonnet ◽  
Vangelis Th. Paschos
Keyword(s):  
Graph Problems

scholarly journals Heuristics for Semirandom Graph Problems

2001 ◽  
Vol 63 (4) ◽  
pp. 639-671 ◽  
Author(s):  
Uriel Feige ◽  
Joe Kilian
Keyword(s):  
Graph Problems

Efficient parallel algorithms for some graph problems

1982 ◽  
Vol 25 (9) ◽  
pp. 659-665 ◽  
Author(s):  
Francis Y. Chin ◽  
John Lam ◽  
I-Ngo Chen
Keyword(s):  
Parallel Algorithms ◽  
Graph Problems

MetaLearning with Graph Neural Networks

2021 ◽  
Vol 23 (2) ◽  
pp. 13-22
Author(s):  
Debmalya Mandal ◽  
Sourav Medya ◽  
Brian Uzzi ◽  
Charu Aggarwal
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Future Research ◽  
Open Problems ◽  
Research Directions ◽  
Graph Problems ◽  
Meta Learning ◽  
Comprehensive Survey ◽  
Future Research Directions ◽  
Graph Neural Networks

Graph Neural Networks (GNNs), a generalization of deep neural networks on graph data have been widely used in various domains, ranging from drug discovery to recommender systems. However, GNNs on such applications are limited when there are few available samples. Meta-learning has been an important framework to address the lack of samples in machine learning, and in recent years, researchers have started to apply meta-learning to GNNs. In this work, we provide a comprehensive survey of different metalearning approaches involving GNNs on various graph problems showing the power of using these two approaches together. We categorize the literature based on proposed architectures, shared representations, and applications. Finally, we discuss several exciting future research directions and open problems.

Global Budget Battles

Science ◽  
1995 ◽  
Vol 270 (5244) ◽  
pp. 1905-1905
Keyword(s):  
Global Budget

Efficient Initial Pool Generation for Weighted Graph Problems Using Parallel Overlap Assembly

2005 ◽  
pp. 215-223 ◽  
Author(s):  
Ji Youn Lee ◽  
Hee-Woong Lim ◽  
Suk-In Yoo ◽  
Byoung-Tak Zhang ◽  
Tai Hyun Park
Keyword(s):  
Weighted Graph ◽  
Graph Problems

scholarly journals Global budget of ethane and regional constraints on U.S. sources

2008 ◽  
Vol 113 (D21) ◽  
Author(s):  
Yaping Xiao ◽  
Jennifer A. Logan ◽  
Daniel J. Jacob ◽  
Rynda C. Hudman ◽  
Robert Yantosca ◽  
...  
Keyword(s):  
Global Budget

Trade-Offs for Dynamic Graph Problems

2008 ◽  
pp. 958-961
Author(s):  
Camil Demetrescu ◽  
Giuseppe F. Italiano
Keyword(s):  
Dynamic Graph ◽  
Graph Problems ◽  
Trade Offs

scholarly journals A State-wide Health IT Infrastructure for Population Health: Building a Community-wide Electronic Platform for Maryland’s All-Payer Global Budget

2017 ◽  
Vol 9 (3) ◽  
Author(s):  
Elham Hatef ◽  
Hadi Kharrazi ◽  
Ed VanBaak ◽  
Marc Falcone ◽  
Lindsey Ferris ◽  
...  
Keyword(s):  
Data Collection ◽  
Real Time ◽  
Population Health ◽  
Information Exchange ◽  
Health Information Exchange ◽  
Smoking Status ◽  
Digital Data ◽  
Health It ◽  
Global Budget ◽  
Health Initiatives

Maryland Department of Health (MDH) has been preparing for alignment of its population health initiatives with Maryland’s unique All-Payer hospital global budget program. In order to operationalize population health initiatives, it is required to identify a starter set of measures addressing community level health interventions and to collect interoperable data for those measures. The broad adoption of electronic health records (EHRs) with ongoing data collection on almost all patients in the state, combined with hospital participation in health information exchange (HIE) initiatives, provides an unprecedented opportunity for near real-time assessment of the health of the communities. MDH’s EHR-based monitoring complements, and perhaps replaces, ad-hoc assessments based on limited surveys, billing, and other administrative data. This article explores the potential expansion of health IT capacity as a method to improve population health across Maryland.First, we propose a progression plan for four selected community-wide population health measures: body mass index, blood pressure, smoking status, and falls-related injuries. We then present an assessment of the current and near real-time availability of digital data in Maryland including the geographic granularity on which each measure can be assessed statewide. Finally, we provide general recommendations to improve interoperable data collection for selected measures over time via the Maryland HIE. This paper is intended to serve as a high- level guiding framework for communities across the US that are undergoing healthcare transformation toward integrated models of care using universal interoperable EHRs.

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